Overview

Dataset statistics

Number of variables36
Number of observations4114
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory288.0 B

Variable types

Numeric23
Categorical13

Warnings

gender has constant value "MALE" Constant
Red has a high cardinality: 1147 distinct values High cardinality
Blue has a high cardinality: 1353 distinct values High cardinality
date has a high cardinality: 399 distinct values High cardinality
location has a high cardinality: 145 distinct values High cardinality
ganador has a high cardinality: 1086 distinct values High cardinality
Unnamed: 0 is highly correlated with #High correlation
# is highly correlated with Unnamed: 0High correlation
B_total_rounds_fought is highly correlated with B_winsHigh correlation
B_wins is highly correlated with B_total_rounds_foughtHigh correlation
B_Weight_lbs is highly correlated with R_Weight_lbsHigh correlation
R_Weight_lbs is highly correlated with B_Weight_lbsHigh correlation
no_of_rounds is highly correlated with genderHigh correlation
better_rank is highly correlated with genderHigh correlation
gender is highly correlated with no_of_rounds and 6 other fieldsHigh correlation
country is highly correlated with genderHigh correlation
weight_class is highly correlated with genderHigh correlation
R_Stance is highly correlated with genderHigh correlation
Winner is highly correlated with genderHigh correlation
B_Stance is highly correlated with genderHigh correlation
date is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
# has unique values Unique
B_losses has 1412 (34.3%) zeros Zeros
B_total_rounds_fought has 854 (20.8%) zeros Zeros
B_wins has 1284 (31.2%) zeros Zeros
R_losses has 937 (22.8%) zeros Zeros
R_total_rounds_fought has 371 (9.0%) zeros Zeros
R_wins has 665 (16.2%) zeros Zeros
lose_streak_dif has 2470 (60.0%) zeros Zeros
win_streak_dif has 2123 (51.6%) zeros Zeros
win_dif has 926 (22.5%) zeros Zeros
loss_dif has 1035 (25.2%) zeros Zeros
height_dif has 712 (17.3%) zeros Zeros
reach_dif has 497 (12.1%) zeros Zeros
age_dif has 293 (7.1%) zeros Zeros

Reproduction

Analysis started2021-03-08 16:26:57.525941
Analysis finished2021-03-08 16:30:47.127545
Duration3 minutes and 49.6 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2376.692513
Minimum0
Maximum4565
Zeros1
Zeros (%)< 0.1%
Memory size32.3 KiB
2021-03-08T17:30:47.406295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile251.65
Q11244.25
median2422.5
Q33530.75
95-th percentile4359.35
Maximum4565
Range4565
Interquartile range (IQR)2286.5

Descriptive statistics

Standard deviation1318.840382
Coefficient of variation (CV)0.5549057672
Kurtosis-1.193705035
Mean2376.692513
Median Absolute Deviation (MAD)1141
Skewness-0.09056249695
Sum9777713
Variance1739339.954
MonotocityStrictly increasing
2021-03-08T17:30:47.718868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
26561
 
< 0.1%
25761
 
< 0.1%
5251
 
< 0.1%
25721
 
< 0.1%
5211
 
< 0.1%
25681
 
< 0.1%
5171
 
< 0.1%
25641
 
< 0.1%
25601
 
< 0.1%
Other values (4104)4104
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
45651
< 0.1%
45641
< 0.1%
45631
< 0.1%
45621
< 0.1%
45611
< 0.1%

#
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2376.692513
Minimum0
Maximum4565
Zeros1
Zeros (%)< 0.1%
Memory size32.3 KiB
2021-03-08T17:30:48.000105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile251.65
Q11244.25
median2422.5
Q33530.75
95-th percentile4359.35
Maximum4565
Range4565
Interquartile range (IQR)2286.5

Descriptive statistics

Standard deviation1318.840382
Coefficient of variation (CV)0.5549057672
Kurtosis-1.193705035
Mean2376.692513
Median Absolute Deviation (MAD)1141
Skewness-0.09056249695
Sum9777713
Variance1739339.954
MonotocityStrictly increasing
2021-03-08T17:30:48.299259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
26561
 
< 0.1%
25761
 
< 0.1%
5251
 
< 0.1%
25721
 
< 0.1%
5211
 
< 0.1%
25681
 
< 0.1%
5171
 
< 0.1%
25641
 
< 0.1%
25601
 
< 0.1%
Other values (4104)4104
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
45651
< 0.1%
45641
< 0.1%
45631
< 0.1%
45621
< 0.1%
45611
< 0.1%

Red
Categorical

HIGH CARDINALITY

Distinct1147
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Donald Cerrone
 
23
Jim Miller
 
21
Dustin Poirier
 
18
Demian Maia
 
18
Joseph Benavidez
 
17
Other values (1142)
4017 

Length

Max length25
Median length13
Mean length13.04788527
Min length7

Characters and Unicode

Total characters53679
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique375 ?
Unique (%)9.1%

Sample

1st rowAlistair Overeem
2nd rowCory Sandhagen
3rd rowAlexandre Pantoja
4th rowDiego Ferreira
5th rowMichael Johnson
ValueCountFrequency (%)
Donald Cerrone23
 
0.6%
Jim Miller21
 
0.5%
Dustin Poirier18
 
0.4%
Demian Maia18
 
0.4%
Joseph Benavidez17
 
0.4%
Mauricio Rua16
 
0.4%
Edson Barboza16
 
0.4%
Ross Pearson16
 
0.4%
Cub Swanson16
 
0.4%
Max Holloway15
 
0.4%
Other values (1137)3938
95.7%
2021-03-08T17:30:48.981798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
john63
 
0.8%
chris56
 
0.7%
anthony51
 
0.6%
silva51
 
0.6%
johnson50
 
0.6%
mike48
 
0.6%
michael45
 
0.5%
thiago41
 
0.5%
matt39
 
0.5%
daniel37
 
0.4%
Other values (1622)7917
94.3%

Most occurring characters

ValueCountFrequency (%)
a5062
 
9.4%
e4445
 
8.3%
4285
 
8.0%
i3714
 
6.9%
n3674
 
6.8%
o3640
 
6.8%
r3452
 
6.4%
l2291
 
4.3%
s2178
 
4.1%
t1615
 
3.0%
Other values (45)19323
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40855
76.1%
Uppercase Letter8505
 
15.8%
Space Separator4285
 
8.0%
Dash Punctuation23
 
< 0.1%
Other Punctuation11
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a5062
12.4%
e4445
10.9%
i3714
 
9.1%
n3674
 
9.0%
o3640
 
8.9%
r3452
 
8.4%
l2291
 
5.6%
s2178
 
5.3%
t1615
 
4.0%
u1370
 
3.4%
Other values (16)9414
23.0%
ValueCountFrequency (%)
M892
 
10.5%
J731
 
8.6%
S652
 
7.7%
C598
 
7.0%
D586
 
6.9%
A576
 
6.8%
B540
 
6.3%
R507
 
6.0%
T408
 
4.8%
P388
 
4.6%
Other values (15)2627
30.9%
ValueCountFrequency (%)
'7
63.6%
.4
36.4%
ValueCountFrequency (%)
4285
100.0%
ValueCountFrequency (%)
-23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49360
92.0%
Common4319
 
8.0%

Most frequent character per script

ValueCountFrequency (%)
a5062
 
10.3%
e4445
 
9.0%
i3714
 
7.5%
n3674
 
7.4%
o3640
 
7.4%
r3452
 
7.0%
l2291
 
4.6%
s2178
 
4.4%
t1615
 
3.3%
u1370
 
2.8%
Other values (41)17919
36.3%
ValueCountFrequency (%)
4285
99.2%
-23
 
0.5%
'7
 
0.2%
.4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII53679
100.0%

Most frequent character per block

ValueCountFrequency (%)
a5062
 
9.4%
e4445
 
8.3%
4285
 
8.0%
i3714
 
6.9%
n3674
 
6.8%
o3640
 
6.8%
r3452
 
6.4%
l2291
 
4.3%
s2178
 
4.1%
t1615
 
3.0%
Other values (45)19323
36.0%

Blue
Categorical

HIGH CARDINALITY

Distinct1353
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Charles Oliveira
 
18
Jeremy Stephens
 
16
Nik Lentz
 
14
Kevin Lee
 
12
Rafael Dos Anjos
 
12
Other values (1348)
4042 

Length

Max length27
Median length13
Mean length13.04326689
Min length7

Characters and Unicode

Total characters53660
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique388 ?
Unique (%)9.4%

Sample

1st rowAlexander Volkov
2nd rowFrankie Edgar
3rd rowManel Kape
4th rowBeneil Dariush
5th rowClay Guida
ValueCountFrequency (%)
Charles Oliveira18
 
0.4%
Jeremy Stephens16
 
0.4%
Nik Lentz14
 
0.3%
Kevin Lee12
 
0.3%
Rafael Dos Anjos12
 
0.3%
Donald Cerrone12
 
0.3%
Sam Alvey12
 
0.3%
Yancy Medeiros11
 
0.3%
Tim Boetsch11
 
0.3%
Jorge Masvidal11
 
0.3%
Other values (1343)3985
96.9%
2021-03-08T17:30:49.756636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chris63
 
0.7%
mike63
 
0.7%
alex55
 
0.7%
john52
 
0.6%
anthony49
 
0.6%
matt47
 
0.6%
silva41
 
0.5%
tim41
 
0.5%
ryan39
 
0.5%
justin37
 
0.4%
Other values (1861)7920
94.2%

Most occurring characters

ValueCountFrequency (%)
a5135
 
9.6%
e4440
 
8.3%
4293
 
8.0%
n3695
 
6.9%
o3610
 
6.7%
i3596
 
6.7%
r3475
 
6.5%
l2344
 
4.4%
s2147
 
4.0%
t1725
 
3.2%
Other values (45)19200
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40847
76.1%
Uppercase Letter8486
 
15.8%
Space Separator4293
 
8.0%
Other Punctuation20
 
< 0.1%
Dash Punctuation14
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a5135
12.6%
e4440
10.9%
n3695
 
9.0%
o3610
 
8.8%
i3596
 
8.8%
r3475
 
8.5%
l2344
 
5.7%
s2147
 
5.3%
t1725
 
4.2%
h1311
 
3.2%
Other values (16)9369
22.9%
ValueCountFrequency (%)
M867
 
10.2%
S702
 
8.3%
J691
 
8.1%
C634
 
7.5%
A586
 
6.9%
D571
 
6.7%
R519
 
6.1%
B513
 
6.0%
T378
 
4.5%
P371
 
4.4%
Other values (15)2654
31.3%
ValueCountFrequency (%)
'15
75.0%
.5
 
25.0%
ValueCountFrequency (%)
4293
100.0%
ValueCountFrequency (%)
-14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49333
91.9%
Common4327
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
a5135
 
10.4%
e4440
 
9.0%
n3695
 
7.5%
o3610
 
7.3%
i3596
 
7.3%
r3475
 
7.0%
l2344
 
4.8%
s2147
 
4.4%
t1725
 
3.5%
h1311
 
2.7%
Other values (41)17855
36.2%
ValueCountFrequency (%)
4293
99.2%
'15
 
0.3%
-14
 
0.3%
.5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII53660
100.0%

Most frequent character per block

ValueCountFrequency (%)
a5135
 
9.6%
e4440
 
8.3%
4293
 
8.0%
n3695
 
6.9%
o3610
 
6.7%
i3596
 
6.7%
r3475
 
6.5%
l2344
 
4.4%
s2147
 
4.0%
t1725
 
3.2%
Other values (45)19200
35.8%

date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct399
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
5/31/2014
 
22
10/4/2014
 
22
6/28/2014
 
21
11/19/2016
 
21
8/23/2014
 
20
Other values (394)
4008 

Length

Max length10
Median length9
Mean length8.967914439
Min length8

Characters and Unicode

Total characters36894
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2/6/2021
2nd row2/6/2021
3rd row2/6/2021
4th row2/6/2021
5th row2/6/2021
ValueCountFrequency (%)
5/31/201422
 
0.5%
10/4/201422
 
0.5%
6/28/201421
 
0.5%
11/19/201621
 
0.5%
8/23/201420
 
0.5%
11/21/201513
 
0.3%
4/6/201313
 
0.3%
1/17/201613
 
0.3%
11/7/201513
 
0.3%
9/21/201313
 
0.3%
Other values (389)3943
95.8%
2021-03-08T17:30:50.476831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5/31/201422
 
0.5%
10/4/201422
 
0.5%
6/28/201421
 
0.5%
11/19/201621
 
0.5%
8/23/201420
 
0.5%
11/21/201513
 
0.3%
4/6/201313
 
0.3%
1/17/201613
 
0.3%
11/7/201513
 
0.3%
9/21/201313
 
0.3%
Other values (389)3943
95.8%

Most occurring characters

ValueCountFrequency (%)
/8228
22.3%
17710
20.9%
27189
19.5%
05382
14.6%
61242
 
3.4%
31232
 
3.3%
81210
 
3.3%
71202
 
3.3%
51190
 
3.2%
41163
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28666
77.7%
Other Punctuation8228
 
22.3%

Most frequent character per category

ValueCountFrequency (%)
17710
26.9%
27189
25.1%
05382
18.8%
61242
 
4.3%
31232
 
4.3%
81210
 
4.2%
71202
 
4.2%
51190
 
4.2%
41163
 
4.1%
91146
 
4.0%
ValueCountFrequency (%)
/8228
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common36894
100.0%

Most frequent character per script

ValueCountFrequency (%)
/8228
22.3%
17710
20.9%
27189
19.5%
05382
14.6%
61242
 
3.4%
31232
 
3.3%
81210
 
3.3%
71202
 
3.3%
51190
 
3.2%
41163
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII36894
100.0%

Most frequent character per block

ValueCountFrequency (%)
/8228
22.3%
17710
20.9%
27189
19.5%
05382
14.6%
61242
 
3.4%
31232
 
3.3%
81210
 
3.3%
71202
 
3.3%
51190
 
3.2%
41163
 
3.2%

location
Categorical

HIGH CARDINALITY

Distinct145
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Las Vegas, Nevada, USA
852 
Abu Dhabi, Abu Dhabi, United Arab Emirates
 
127
Newark, New Jersey, USA
 
74
London, England, United Kingdom
 
72
Toronto, Ontario, Canada
 
70
Other values (140)
2919 

Length

Max length43
Median length23
Mean length25.154351
Min length12

Characters and Unicode

Total characters103485
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLas Vegas, Nevada, USA
2nd rowLas Vegas, Nevada, USA
3rd rowLas Vegas, Nevada, USA
4th rowLas Vegas, Nevada, USA
5th rowLas Vegas, Nevada, USA
ValueCountFrequency (%)
Las Vegas, Nevada, USA852
 
20.7%
Abu Dhabi, Abu Dhabi, United Arab Emirates127
 
3.1%
Newark, New Jersey, USA74
 
1.8%
London, England, United Kingdom72
 
1.8%
Toronto, Ontario, Canada70
 
1.7%
Stockholm, Sweden70
 
1.7%
Chicago, Illinois, USA69
 
1.7%
Boston, Massachusetts, USA69
 
1.7%
Rio de Janeiro, Brazil66
 
1.6%
Sao Paulo, Sao Paulo, Brazil55
 
1.3%
Other values (135)2590
63.0%
2021-03-08T17:30:51.168751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa2429
 
15.9%
vegas852
 
5.6%
las852
 
5.6%
nevada852
 
5.6%
new406
 
2.7%
brazil383
 
2.5%
canada310
 
2.0%
united299
 
2.0%
abu271
 
1.8%
dhabi271
 
1.8%
Other values (246)8352
54.7%

Most occurring characters

ValueCountFrequency (%)
a11667
 
11.3%
11163
 
10.8%
,7792
 
7.5%
e6756
 
6.5%
i5640
 
5.5%
n4925
 
4.8%
o4895
 
4.7%
s4401
 
4.3%
r3962
 
3.8%
l3494
 
3.4%
Other values (45)38790
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64493
62.3%
Uppercase Letter19998
 
19.3%
Space Separator11163
 
10.8%
Other Punctuation7799
 
7.5%
Dash Punctuation32
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a11667
18.1%
e6756
10.5%
i5640
8.7%
n4925
 
7.6%
o4895
 
7.6%
s4401
 
6.8%
r3962
 
6.1%
l3494
 
5.4%
t3083
 
4.8%
d2972
 
4.6%
Other values (16)12698
19.7%
ValueCountFrequency (%)
A3446
17.2%
S3366
16.8%
U2782
13.9%
N1551
7.8%
C1187
 
5.9%
L1085
 
5.4%
V988
 
4.9%
B799
 
4.0%
M625
 
3.1%
D531
 
2.7%
Other values (15)3638
18.2%
ValueCountFrequency (%)
,7792
99.9%
.7
 
0.1%
ValueCountFrequency (%)
11163
100.0%
ValueCountFrequency (%)
-32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin84491
81.6%
Common18994
 
18.4%

Most frequent character per script

ValueCountFrequency (%)
a11667
 
13.8%
e6756
 
8.0%
i5640
 
6.7%
n4925
 
5.8%
o4895
 
5.8%
s4401
 
5.2%
r3962
 
4.7%
l3494
 
4.1%
A3446
 
4.1%
S3366
 
4.0%
Other values (41)31939
37.8%
ValueCountFrequency (%)
11163
58.8%
,7792
41.0%
-32
 
0.2%
.7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII103485
100.0%

Most frequent character per block

ValueCountFrequency (%)
a11667
 
11.3%
11163
 
10.8%
,7792
 
7.5%
e6756
 
6.5%
i5640
 
5.5%
n4925
 
4.8%
o4895
 
4.7%
s4401
 
4.3%
r3962
 
3.8%
l3494
 
3.4%
Other values (45)38790
37.5%

country
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
USA
2208 
Brazil
375 
Canada
310 
USA
221 
United Kingdom
 
155
Other values (23)
845 

Length

Max length21
Median length4
Mean length6.201750122
Min length3

Characters and Unicode

Total characters25514
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA
ValueCountFrequency (%)
USA2208
53.7%
Brazil375
 
9.1%
Canada310
 
7.5%
USA221
 
5.4%
United Kingdom155
 
3.8%
Australia147
 
3.6%
United Arab Emirates117
 
2.8%
Sweden70
 
1.7%
Mexico62
 
1.5%
China52
 
1.3%
Other values (18)397
 
9.6%
2021-03-08T17:30:51.810532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa2429
52.6%
brazil383
 
8.3%
canada310
 
6.7%
united299
 
6.5%
kingdom155
 
3.4%
australia147
 
3.2%
emirates144
 
3.1%
arab144
 
3.1%
sweden70
 
1.5%
mexico62
 
1.3%
Other values (20)476
 
10.3%

Most occurring characters

ValueCountFrequency (%)
4273
16.7%
U2738
10.7%
A2731
10.7%
S2559
10.0%
a2367
9.3%
i1394
 
5.5%
n1160
 
4.5%
r1015
 
4.0%
e944
 
3.7%
d922
 
3.6%
Other values (29)5411
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11764
46.1%
Uppercase Letter9477
37.1%
Space Separator4273
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
a2367
20.1%
i1394
11.8%
n1160
9.9%
r1015
8.6%
e944
 
8.0%
d922
 
7.8%
t657
 
5.6%
l651
 
5.5%
z394
 
3.3%
s393
 
3.3%
Other values (12)1867
15.9%
ValueCountFrequency (%)
U2738
28.9%
A2731
28.8%
S2559
27.0%
C395
 
4.2%
B383
 
4.0%
K177
 
1.9%
E144
 
1.5%
M62
 
0.7%
G52
 
0.5%
N51
 
0.5%
Other values (6)185
 
2.0%
ValueCountFrequency (%)
4273
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21241
83.3%
Common4273
 
16.7%

Most frequent character per script

ValueCountFrequency (%)
U2738
12.9%
A2731
12.9%
S2559
12.0%
a2367
11.1%
i1394
 
6.6%
n1160
 
5.5%
r1015
 
4.8%
e944
 
4.4%
d922
 
4.3%
t657
 
3.1%
Other values (28)4754
22.4%
ValueCountFrequency (%)
4273
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25514
100.0%

Most frequent character per block

ValueCountFrequency (%)
4273
16.7%
U2738
10.7%
A2731
10.7%
S2559
10.0%
a2367
9.3%
i1394
 
5.5%
n1160
 
4.5%
r1015
 
4.0%
e944
 
3.7%
d922
 
3.6%
Other values (29)5411
21.2%

Winner
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Red
2400 
Blue
1714 

Length

Max length4
Median length3
Mean length3.416626155
Min length3

Characters and Unicode

Total characters14056
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowRed
3rd rowRed
4th rowBlue
5th rowBlue
ValueCountFrequency (%)
Red2400
58.3%
Blue1714
41.7%
2021-03-08T17:30:52.391807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-08T17:30:52.579322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
red2400
58.3%
blue1714
41.7%

Most occurring characters

ValueCountFrequency (%)
e4114
29.3%
R2400
17.1%
d2400
17.1%
B1714
12.2%
l1714
12.2%
u1714
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9942
70.7%
Uppercase Letter4114
29.3%

Most frequent character per category

ValueCountFrequency (%)
e4114
41.4%
d2400
24.1%
l1714
17.2%
u1714
17.2%
ValueCountFrequency (%)
R2400
58.3%
B1714
41.7%

Most occurring scripts

ValueCountFrequency (%)
Latin14056
100.0%

Most frequent character per script

ValueCountFrequency (%)
e4114
29.3%
R2400
17.1%
d2400
17.1%
B1714
12.2%
l1714
12.2%
u1714
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII14056
100.0%

Most frequent character per block

ValueCountFrequency (%)
e4114
29.3%
R2400
17.1%
d2400
17.1%
B1714
12.2%
l1714
12.2%
u1714
12.2%

weight_class
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Lightweight
815 
Welterweight
789 
Middleweight
550 
Featherweight
530 
Bantamweight
451 
Other values (4)
979 

Length

Max length17
Median length12
Mean length12.13247448
Min length9

Characters and Unicode

Total characters49913
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHeavyweight
2nd rowBantamweight
3rd rowFlyweight
4th rowLightweight
5th rowLightweight
ValueCountFrequency (%)
Lightweight815
19.8%
Welterweight789
19.2%
Middleweight550
13.4%
Featherweight530
12.9%
Bantamweight451
11.0%
Light Heavyweight370
9.0%
Heavyweight357
8.7%
Flyweight221
 
5.4%
Catch Weight31
 
0.8%
2021-03-08T17:30:53.142862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-08T17:30:53.379277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
lightweight815
18.1%
welterweight789
17.5%
heavyweight727
16.1%
middleweight550
12.2%
featherweight530
11.7%
bantamweight451
10.0%
light370
8.2%
flyweight221
 
4.9%
weight31
 
0.7%
catch31
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e8029
16.1%
t7100
14.2%
h5860
11.7%
i5849
11.7%
g5299
10.6%
w4083
8.2%
a2190
 
4.4%
l1560
 
3.1%
r1319
 
2.6%
L1185
 
2.4%
Other values (13)7439
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter44997
90.2%
Uppercase Letter4515
 
9.0%
Space Separator401
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
e8029
17.8%
t7100
15.8%
h5860
13.0%
i5849
13.0%
g5299
11.8%
w4083
9.1%
a2190
 
4.9%
l1560
 
3.5%
r1319
 
2.9%
d1100
 
2.4%
Other values (5)2608
 
5.8%
ValueCountFrequency (%)
L1185
26.2%
W820
18.2%
F751
16.6%
H727
16.1%
M550
12.2%
B451
 
10.0%
C31
 
0.7%
ValueCountFrequency (%)
401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49512
99.2%
Common401
 
0.8%

Most frequent character per script

ValueCountFrequency (%)
e8029
16.2%
t7100
14.3%
h5860
11.8%
i5849
11.8%
g5299
10.7%
w4083
8.2%
a2190
 
4.4%
l1560
 
3.2%
r1319
 
2.7%
L1185
 
2.4%
Other values (12)7038
14.2%
ValueCountFrequency (%)
401
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII49913
100.0%

Most frequent character per block

ValueCountFrequency (%)
e8029
16.1%
t7100
14.2%
h5860
11.7%
i5849
11.7%
g5299
10.6%
w4083
8.2%
a2190
 
4.4%
l1560
 
3.1%
r1319
 
2.6%
L1185
 
2.4%
Other values (13)7439
14.9%

gender
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
MALE
4114 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16456
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowMALE
ValueCountFrequency (%)
MALE4114
100.0%
2021-03-08T17:30:54.148313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-08T17:30:54.361549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
male4114
100.0%

Most occurring characters

ValueCountFrequency (%)
M4114
25.0%
A4114
25.0%
L4114
25.0%
E4114
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter16456
100.0%

Most frequent character per category

ValueCountFrequency (%)
M4114
25.0%
A4114
25.0%
L4114
25.0%
E4114
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16456
100.0%

Most frequent character per script

ValueCountFrequency (%)
M4114
25.0%
A4114
25.0%
L4114
25.0%
E4114
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16456
100.0%

Most frequent character per block

ValueCountFrequency (%)
M4114
25.0%
A4114
25.0%
L4114
25.0%
E4114
25.0%

no_of_rounds
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
3
3741 
5
 
354
4
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4114
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row3
4th row3
5th row3
ValueCountFrequency (%)
33741
90.9%
5354
 
8.6%
419
 
0.5%
2021-03-08T17:30:54.980127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-08T17:30:55.214650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
33741
90.9%
5354
 
8.6%
419
 
0.5%

Most occurring characters

ValueCountFrequency (%)
33741
90.9%
5354
 
8.6%
419
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4114
100.0%

Most frequent character per category

ValueCountFrequency (%)
33741
90.9%
5354
 
8.6%
419
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common4114
100.0%

Most frequent character per script

ValueCountFrequency (%)
33741
90.9%
5354
 
8.6%
419
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4114
100.0%

Most frequent character per block

ValueCountFrequency (%)
33741
90.9%
5354
 
8.6%
419
 
0.5%

B_losses
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.756927564
Minimum0
Maximum15
Zeros1412
Zeros (%)34.3%
Memory size32.3 KiB
2021-03-08T17:30:55.485181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.115836319
Coefficient of variation (CV)1.204282044
Kurtosis4.182647625
Mean1.756927564
Median Absolute Deviation (MAD)1
Skewness1.828671624
Sum7228
Variance4.476763327
MonotocityNot monotonic
2021-03-08T17:30:55.808832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01412
34.3%
11019
24.8%
2630
15.3%
3362
 
8.8%
4283
 
6.9%
5148
 
3.6%
688
 
2.1%
759
 
1.4%
845
 
1.1%
931
 
0.8%
Other values (6)37
 
0.9%
ValueCountFrequency (%)
01412
34.3%
11019
24.8%
2630
15.3%
3362
 
8.8%
4283
 
6.9%
ValueCountFrequency (%)
152
 
< 0.1%
141
 
< 0.1%
133
 
0.1%
123
 
0.1%
1112
0.3%

B_total_rounds_fought
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct81
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.89523578
Minimum0
Maximum97
Zeros854
Zeros (%)20.8%
Memory size32.3 KiB
2021-03-08T17:30:56.185195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q315
95-th percentile38
Maximum97
Range97
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.28307309
Coefficient of variation (CV)1.219163436
Kurtosis5.500482333
Mean10.89523578
Median Absolute Deviation (MAD)6
Skewness2.088137122
Sum44823
Variance176.4400308
MonotocityNot monotonic
2021-03-08T17:30:56.497119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0854
20.8%
3384
 
9.3%
6229
 
5.6%
1189
 
4.6%
7157
 
3.8%
9156
 
3.8%
4154
 
3.7%
2138
 
3.4%
5137
 
3.3%
10119
 
2.9%
Other values (71)1597
38.8%
ValueCountFrequency (%)
0854
20.8%
1189
 
4.6%
2138
 
3.4%
3384
9.3%
4154
 
3.7%
ValueCountFrequency (%)
971
< 0.1%
941
< 0.1%
911
< 0.1%
891
< 0.1%
871
< 0.1%

B_wins
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.958920758
Minimum0
Maximum31
Zeros1284
Zeros (%)31.2%
Memory size32.3 KiB
2021-03-08T17:30:56.781855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10
Maximum31
Range31
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.706076178
Coefficient of variation (CV)1.252509438
Kurtosis4.680130432
Mean2.958920758
Median Absolute Deviation (MAD)2
Skewness1.919464926
Sum12173
Variance13.73500063
MonotocityNot monotonic
2021-03-08T17:30:57.077038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
01284
31.2%
1724
17.6%
2454
 
11.0%
3383
 
9.3%
4292
 
7.1%
5194
 
4.7%
6186
 
4.5%
7125
 
3.0%
9107
 
2.6%
8104
 
2.5%
Other values (16)261
 
6.3%
ValueCountFrequency (%)
01284
31.2%
1724
17.6%
2454
 
11.0%
3383
 
9.3%
4292
 
7.1%
ValueCountFrequency (%)
311
< 0.1%
291
< 0.1%
232
< 0.1%
222
< 0.1%
212
< 0.1%

B_Stance
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Orthodox
3044 
Southpaw
876 
Switch
 
192
Switch
 
1
Open Stance
 
1

Length

Max length11
Median length8
Mean length7.90714633
Min length6

Characters and Unicode

Total characters32530
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowOrthodox
2nd rowOrthodox
3rd rowSouthpaw
4th rowSouthpaw
5th rowOrthodox
ValueCountFrequency (%)
Orthodox3044
74.0%
Southpaw876
 
21.3%
Switch192
 
4.7%
Switch 1
 
< 0.1%
Open Stance1
 
< 0.1%
2021-03-08T17:30:57.739120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-08T17:30:57.958031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
orthodox3044
74.0%
southpaw876
 
21.3%
switch193
 
4.7%
stance1
 
< 0.1%
open1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o6964
21.4%
t4114
12.6%
h4113
12.6%
O3045
9.4%
r3044
9.4%
d3044
9.4%
x3044
9.4%
S1070
 
3.3%
w1069
 
3.3%
p877
 
2.7%
Other values (7)2146
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28413
87.3%
Uppercase Letter4115
 
12.6%
Space Separator2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o6964
24.5%
t4114
14.5%
h4113
14.5%
r3044
10.7%
d3044
10.7%
x3044
10.7%
w1069
 
3.8%
p877
 
3.1%
a877
 
3.1%
u876
 
3.1%
Other values (4)391
 
1.4%
ValueCountFrequency (%)
O3045
74.0%
S1070
 
26.0%
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin32528
> 99.9%
Common2
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
o6964
21.4%
t4114
12.6%
h4113
12.6%
O3045
9.4%
r3044
9.4%
d3044
9.4%
x3044
9.4%
S1070
 
3.3%
w1069
 
3.3%
p877
 
2.7%
Other values (6)2144
 
6.6%
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII32530
100.0%

Most frequent character per block

ValueCountFrequency (%)
o6964
21.4%
t4114
12.6%
h4113
12.6%
O3045
9.4%
r3044
9.4%
d3044
9.4%
x3044
9.4%
S1070
 
3.3%
w1069
 
3.3%
p877
 
2.7%
Other values (7)2146
 
6.6%

B_Height_cms
Real number (ℝ≥0)

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.5532523
Minimum157.48
Maximum210.82
Zeros0
Zeros (%)0.0%
Memory size32.3 KiB
2021-03-08T17:30:58.271491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum157.48
5-th percentile167.64
Q1172.72
median180.34
Q3185.42
95-th percentile193.04
Maximum210.82
Range53.34
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation8.036585888
Coefficient of variation (CV)0.04475878763
Kurtosis-0.2388826865
Mean179.5532523
Median Absolute Deviation (MAD)5.08
Skewness0.01942139806
Sum738682.08
Variance64.58671274
MonotocityNot monotonic
2021-03-08T17:30:59.541783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
177.8526
12.8%
182.88507
12.3%
180.34445
10.8%
185.42383
9.3%
172.72377
9.2%
175.26372
9.0%
187.96302
7.3%
190.5278
6.8%
170.18257
6.2%
167.64235
5.7%
Other values (11)432
10.5%
ValueCountFrequency (%)
157.482
 
< 0.1%
160.0231
 
0.8%
162.5655
 
1.3%
165.1109
2.6%
167.64235
5.7%
ValueCountFrequency (%)
210.826
 
0.1%
203.22
 
< 0.1%
200.6613
 
0.3%
198.1222
0.5%
195.5844
1.1%

B_Reach_cms
Real number (ℝ≥0)

Distinct53
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.197212
Minimum0
Maximum213.36
Zeros1
Zeros (%)< 0.1%
Memory size32.3 KiB
2021-03-08T17:30:59.870100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile167.64
Q1177.8
median185
Q3190.5
95-th percentile200.66
Maximum213.36
Range213.36
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation9.904181069
Coefficient of variation (CV)0.05376944072
Kurtosis28.56625026
Mean184.197212
Median Absolute Deviation (MAD)7.2
Skewness-1.52800228
Sum757787.33
Variance98.09280266
MonotocityNot monotonic
2021-03-08T17:31:00.198217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177.8430
10.5%
185.42413
10.0%
182.88411
10.0%
187.96406
9.9%
180.34401
9.7%
190.5353
8.6%
193.04296
 
7.2%
172.72195
 
4.7%
175.26193
 
4.7%
195.58191
 
4.6%
Other values (43)825
20.1%
ValueCountFrequency (%)
01
 
< 0.1%
157.483
 
0.1%
160.0217
0.4%
162.5637
0.9%
1632
 
< 0.1%
ValueCountFrequency (%)
213.3610
0.2%
210.825
 
0.1%
208.2820
0.5%
2062
 
< 0.1%
205.7424
0.6%

B_Weight_lbs
Real number (ℝ≥0)

HIGH CORRELATION

Distinct34
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.4686437
Minimum125
Maximum265
Zeros0
Zeros (%)0.0%
Memory size32.3 KiB
2021-03-08T17:31:00.512584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q1145
median170
Q3185
95-th percentile250
Maximum265
Range140
Interquartile range (IQR)40

Descriptive statistics

Standard deviation32.94422216
Coefficient of variation (CV)0.1943971548
Kurtosis1.088847489
Mean169.4686437
Median Absolute Deviation (MAD)15
Skewness1.168497117
Sum697194
Variance1085.321774
MonotocityNot monotonic
2021-03-08T17:31:00.796046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
170826
20.1%
155823
20.0%
185514
12.5%
135499
12.1%
145497
12.1%
205402
9.8%
125211
 
5.1%
26572
 
1.8%
25042
 
1.0%
24034
 
0.8%
Other values (24)194
 
4.7%
ValueCountFrequency (%)
125211
 
5.1%
135499
12.1%
1402
 
< 0.1%
145497
12.1%
155823
20.0%
ValueCountFrequency (%)
26572
1.8%
26422
 
0.5%
2637
 
0.2%
2622
 
< 0.1%
26031
0.8%

R_losses
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.424404473
Minimum0
Maximum17
Zeros937
Zeros (%)22.8%
Memory size32.3 KiB
2021-03-08T17:31:01.093067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile7
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.491560752
Coefficient of variation (CV)1.027700114
Kurtosis3.373688636
Mean2.424404473
Median Absolute Deviation (MAD)1
Skewness1.609260798
Sum9974
Variance6.20787498
MonotocityNot monotonic
2021-03-08T17:31:01.333824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0937
22.8%
1932
22.7%
2703
17.1%
3524
12.7%
4321
 
7.8%
5237
 
5.8%
6168
 
4.1%
787
 
2.1%
875
 
1.8%
949
 
1.2%
Other values (8)81
 
2.0%
ValueCountFrequency (%)
0937
22.8%
1932
22.7%
2703
17.1%
3524
12.7%
4321
 
7.8%
ValueCountFrequency (%)
172
 
< 0.1%
163
 
0.1%
153
 
0.1%
143
 
0.1%
138
0.2%

R_total_rounds_fought
Real number (ℝ≥0)

ZEROS

Distinct88
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.23796791
Minimum0
Maximum448
Zeros371
Zeros (%)9.0%
Memory size32.3 KiB
2021-03-08T17:31:01.627161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q324
95-th percentile48
Maximum448
Range448
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.12604003
Coefficient of variation (CV)1.054691087
Kurtosis99.1620975
Mean16.23796791
Median Absolute Deviation (MAD)8
Skewness5.03085768
Sum66803
Variance293.3012471
MonotocityNot monotonic
2021-03-08T17:31:01.939105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0371
 
9.0%
3294
 
7.1%
6208
 
5.1%
4157
 
3.8%
5147
 
3.6%
1145
 
3.5%
9143
 
3.5%
7137
 
3.3%
2125
 
3.0%
10122
 
3.0%
Other values (78)2265
55.1%
ValueCountFrequency (%)
0371
9.0%
1145
 
3.5%
2125
 
3.0%
3294
7.1%
4157
3.8%
ValueCountFrequency (%)
4481
< 0.1%
951
< 0.1%
882
< 0.1%
861
< 0.1%
852
< 0.1%

R_wins
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.410792416
Minimum0
Maximum33
Zeros665
Zeros (%)16.2%
Memory size32.3 KiB
2021-03-08T17:31:02.236118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum33
Range33
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.32774034
Coefficient of variation (CV)0.981170713
Kurtosis2.615162333
Mean4.410792416
Median Absolute Deviation (MAD)2
Skewness1.416968063
Sum18146
Variance18.72933645
MonotocityNot monotonic
2021-03-08T17:31:02.489143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0665
16.2%
1629
15.3%
2484
11.8%
3420
10.2%
4347
8.4%
5281
6.8%
6227
 
5.5%
7201
 
4.9%
9165
 
4.0%
8163
 
4.0%
Other values (18)532
12.9%
ValueCountFrequency (%)
0665
16.2%
1629
15.3%
2484
11.8%
3420
10.2%
4347
8.4%
ValueCountFrequency (%)
331
< 0.1%
322
< 0.1%
291
< 0.1%
271
< 0.1%
261
< 0.1%

R_Stance
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Orthodox
3088 
Southpaw
877 
Switch
 
145
Open Stance
 
4

Length

Max length11
Median length8
Mean length7.932425863
Min length6

Characters and Unicode

Total characters32634
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrthodox
2nd rowSwitch
3rd rowOrthodox
4th rowOrthodox
5th rowSouthpaw
ValueCountFrequency (%)
Orthodox3088
75.1%
Southpaw877
 
21.3%
Switch145
 
3.5%
Open Stance4
 
0.1%
2021-03-08T17:31:03.117644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-08T17:31:03.320763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
orthodox3088
75.0%
southpaw877
 
21.3%
switch145
 
3.5%
stance4
 
0.1%
open4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o7053
21.6%
t4114
12.6%
h4110
12.6%
O3092
9.5%
r3088
9.5%
d3088
9.5%
x3088
9.5%
S1026
 
3.1%
w1022
 
3.1%
p881
 
2.7%
Other values (7)2072
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28512
87.4%
Uppercase Letter4118
 
12.6%
Space Separator4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
o7053
24.7%
t4114
14.4%
h4110
14.4%
r3088
10.8%
d3088
10.8%
x3088
10.8%
w1022
 
3.6%
p881
 
3.1%
a881
 
3.1%
u877
 
3.1%
Other values (4)310
 
1.1%
ValueCountFrequency (%)
O3092
75.1%
S1026
 
24.9%
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin32630
> 99.9%
Common4
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
o7053
21.6%
t4114
12.6%
h4110
12.6%
O3092
9.5%
r3088
9.5%
d3088
9.5%
x3088
9.5%
S1026
 
3.1%
w1022
 
3.1%
p881
 
2.7%
Other values (6)2068
 
6.3%
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII32634
100.0%

Most frequent character per block

ValueCountFrequency (%)
o7053
21.6%
t4114
12.6%
h4110
12.6%
O3092
9.5%
r3088
9.5%
d3088
9.5%
x3088
9.5%
S1026
 
3.1%
w1022
 
3.1%
p881
 
2.7%
Other values (7)2072
 
6.3%

R_Height_cms
Real number (ℝ≥0)

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.3899174
Minimum157.48
Maximum210.82
Zeros0
Zeros (%)0.0%
Memory size32.3 KiB
2021-03-08T17:31:03.571039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum157.48
5-th percentile165.1
Q1172.72
median180.34
Q3185.42
95-th percentile193.04
Maximum210.82
Range53.34
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation8.317455043
Coefficient of variation (CV)0.04636523148
Kurtosis-0.0518060038
Mean179.3899174
Median Absolute Deviation (MAD)5.08
Skewness0.0497891138
Sum738010.12
Variance69.18005839
MonotocityNot monotonic
2021-03-08T17:31:03.821064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
182.88497
12.1%
180.34445
10.8%
177.8439
10.7%
185.42438
10.6%
175.26394
9.6%
172.72363
8.8%
187.96292
7.1%
167.64276
6.7%
190.5258
6.3%
170.18249
6.1%
Other values (10)463
11.3%
ValueCountFrequency (%)
157.482
 
< 0.1%
160.0245
 
1.1%
162.5672
 
1.8%
165.1108
 
2.6%
167.64276
6.7%
ValueCountFrequency (%)
210.8214
 
0.3%
200.6611
 
0.3%
198.1226
0.6%
1961
 
< 0.1%
195.5838
0.9%

R_Reach_cms
Real number (ℝ≥0)

Distinct46
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.3334978
Minimum157.48
Maximum214.63
Zeros0
Zeros (%)0.0%
Memory size32.3 KiB
2021-03-08T17:31:04.103909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum157.48
5-th percentile167.64
Q1177.8
median185.42
Q3190.5
95-th percentile200.66
Maximum214.63
Range57.15
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation9.829221806
Coefficient of variation (CV)0.05332303636
Kurtosis-0.02314742147
Mean184.3334978
Median Absolute Deviation (MAD)7.62
Skewness0.1392265521
Sum758348.01
Variance96.6136013
MonotocityNot monotonic
2021-03-08T17:31:04.369400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
185.42438
10.6%
177.8422
10.3%
180.34406
9.9%
182.88383
9.3%
187.96379
9.2%
190.5375
9.1%
193.04286
 
7.0%
172.72204
 
5.0%
175.26195
 
4.7%
195.58190
 
4.6%
Other values (36)836
20.3%
ValueCountFrequency (%)
157.481
 
< 0.1%
160.0216
 
0.4%
162.5642
1.0%
1651
 
< 0.1%
165.174
1.8%
ValueCountFrequency (%)
214.631
 
< 0.1%
213.3629
0.7%
2111
 
< 0.1%
210.828
 
0.2%
208.2815
0.4%

R_Weight_lbs
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.912737
Minimum125
Maximum265
Zeros0
Zeros (%)0.0%
Memory size32.3 KiB
2021-03-08T17:31:04.668964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q1145
median170
Q3185
95-th percentile250
Maximum265
Range140
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.25902612
Coefficient of variation (CV)0.1957418067
Kurtosis1.02970049
Mean169.912737
Median Absolute Deviation (MAD)15
Skewness1.156203405
Sum699021
Variance1106.162819
MonotocityNot monotonic
2021-03-08T17:31:04.934606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
170843
20.5%
155789
19.2%
185541
13.2%
145499
12.1%
135489
11.9%
205376
9.1%
125214
 
5.2%
26581
 
2.0%
24055
 
1.3%
25036
 
0.9%
Other values (22)191
 
4.6%
ValueCountFrequency (%)
125214
 
5.2%
135489
11.9%
1401
 
< 0.1%
145499
12.1%
155789
19.2%
ValueCountFrequency (%)
26581
2.0%
26420
 
0.5%
26312
 
0.3%
2623
 
0.1%
26028
 
0.7%

R_age
Real number (ℝ≥0)

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.05007292
Minimum19
Maximum47
Zeros0
Zeros (%)0.0%
Memory size32.3 KiB
2021-03-08T17:31:05.232421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile24
Q127
median30
Q333
95-th percentile37
Maximum47
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.084735986
Coefficient of variation (CV)0.1359309841
Kurtosis-0.08350165389
Mean30.05007292
Median Absolute Deviation (MAD)3
Skewness0.2707695181
Sum123626
Variance16.68506807
MonotocityNot monotonic
2021-03-08T17:31:05.482584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
30403
9.8%
29394
9.6%
31371
9.0%
28349
 
8.5%
32347
 
8.4%
27335
 
8.1%
26293
 
7.1%
33253
 
6.1%
34248
 
6.0%
25208
 
5.1%
Other values (18)913
22.2%
ValueCountFrequency (%)
194
 
0.1%
2012
 
0.3%
2118
 
0.4%
2251
1.2%
2390
2.2%
ValueCountFrequency (%)
471
 
< 0.1%
451
 
< 0.1%
443
 
0.1%
432
 
< 0.1%
4210
0.2%

B_age
Real number (ℝ≥0)

Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.48298493
Minimum19
Maximum47
Zeros0
Zeros (%)0.0%
Memory size32.3 KiB
2021-03-08T17:31:05.765306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile23
Q127
median29
Q332
95-th percentile36
Maximum47
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.000161343
Coefficient of variation (CV)0.1356769456
Kurtosis0.2586338391
Mean29.48298493
Median Absolute Deviation (MAD)3
Skewness0.421937817
Sum121293
Variance16.00129077
MonotocityNot monotonic
2021-03-08T17:31:05.999690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
28443
10.8%
29413
10.0%
30394
9.6%
31373
9.1%
27359
8.7%
26316
 
7.7%
32284
 
6.9%
25251
 
6.1%
33229
 
5.6%
34190
 
4.6%
Other values (19)862
21.0%
ValueCountFrequency (%)
191
 
< 0.1%
2013
 
0.3%
2126
 
0.6%
2261
1.5%
23131
3.2%
ValueCountFrequency (%)
471
 
< 0.1%
461
 
< 0.1%
452
 
< 0.1%
443
0.1%
437
0.2%

lose_streak_dif
Real number (ℝ)

ZEROS

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1239669421
Minimum-5
Maximum6
Zeros2470
Zeros (%)60.0%
Memory size32.3 KiB
2021-03-08T17:31:06.253993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile-1
Q10
median0
Q30
95-th percentile2
Maximum6
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9855233262
Coefficient of variation (CV)7.949888165
Kurtosis3.836339084
Mean0.1239669421
Median Absolute Deviation (MAD)0
Skewness0.1876736029
Sum510
Variance0.9712562265
MonotocityNot monotonic
2021-03-08T17:31:06.472755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
02470
60.0%
1696
 
16.9%
-1477
 
11.6%
2213
 
5.2%
-2122
 
3.0%
365
 
1.6%
-336
 
0.9%
417
 
0.4%
-412
 
0.3%
-53
 
0.1%
Other values (2)3
 
0.1%
ValueCountFrequency (%)
-53
 
0.1%
-412
 
0.3%
-336
 
0.9%
-2122
 
3.0%
-1477
11.6%
ValueCountFrequency (%)
62
 
< 0.1%
51
 
< 0.1%
417
 
0.4%
365
 
1.6%
2213
5.2%

win_streak_dif
Real number (ℝ)

ZEROS

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.183033544
Minimum-12
Maximum9
Zeros2123
Zeros (%)51.6%
Memory size32.3 KiB
2021-03-08T17:31:06.738601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-3
Q1-1
median0
Q30
95-th percentile2
Maximum9
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.741442607
Coefficient of variation (CV)-9.514335837
Kurtosis7.988943079
Mean-0.183033544
Median Absolute Deviation (MAD)0
Skewness-1.074156081
Sum-753
Variance3.032622354
MonotocityNot monotonic
2021-03-08T17:31:06.973109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
02123
51.6%
-1619
 
15.0%
1443
 
10.8%
-2271
 
6.6%
2202
 
4.9%
-3121
 
2.9%
399
 
2.4%
455
 
1.3%
-449
 
1.2%
-534
 
0.8%
Other values (12)98
 
2.4%
ValueCountFrequency (%)
-122
 
< 0.1%
-116
0.1%
-106
0.1%
-93
 
0.1%
-813
0.3%
ValueCountFrequency (%)
91
 
< 0.1%
82
 
< 0.1%
74
 
0.1%
612
0.3%
521
0.5%

win_dif
Real number (ℝ)

ZEROS

Distinct41
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.451871658
Minimum-28
Maximum23
Zeros926
Zeros (%)22.5%
Memory size32.3 KiB
2021-03-08T17:31:07.317902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-28
5-th percentile-9
Q1-3
median-1
Q30
95-th percentile5
Maximum23
Range51
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.129586152
Coefficient of variation (CV)-2.844319007
Kurtosis3.706162297
Mean-1.451871658
Median Absolute Deviation (MAD)2
Skewness-0.4048368144
Sum-5973
Variance17.05348179
MonotocityNot monotonic
2021-03-08T17:31:07.614763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0926
22.5%
-1588
14.3%
-2411
10.0%
-3327
 
7.9%
1298
 
7.2%
-4241
 
5.9%
2188
 
4.6%
-5171
 
4.2%
-6145
 
3.5%
3113
 
2.7%
Other values (31)706
17.2%
ValueCountFrequency (%)
-281
< 0.1%
-271
< 0.1%
-262
< 0.1%
-221
< 0.1%
-202
< 0.1%
ValueCountFrequency (%)
231
 
< 0.1%
191
 
< 0.1%
154
 
0.1%
143
 
0.1%
1310
0.2%

loss_dif
Real number (ℝ)

ZEROS

Distinct33
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5065629558
Minimum-17
Maximum16
Zeros1035
Zeros (%)25.2%
Memory size32.3 KiB
2021-03-08T17:31:07.913691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-17
5-th percentile-4
Q1-1
median0
Q32
95-th percentile5
Maximum16
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.853856796
Coefficient of variation (CV)5.633765288
Kurtosis3.168122293
Mean0.5065629558
Median Absolute Deviation (MAD)1
Skewness-0.1046309521
Sum2084
Variance8.144498614
MonotocityNot monotonic
2021-03-08T17:31:08.186431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
01035
25.2%
1673
16.4%
-1468
11.4%
2446
10.8%
-2313
 
7.6%
3303
 
7.4%
4175
 
4.3%
-3147
 
3.6%
5113
 
2.7%
-488
 
2.1%
Other values (23)353
 
8.6%
ValueCountFrequency (%)
-171
< 0.1%
-161
< 0.1%
-151
< 0.1%
-141
< 0.1%
-131
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
142
< 0.1%
131
 
< 0.1%
124
0.1%
113
0.1%

height_dif
Real number (ℝ)

ZEROS

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1215264949
Minimum-187.96
Maximum30.48
Zeros712
Zeros (%)17.3%
Memory size32.3 KiB
2021-03-08T17:31:08.494721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-187.96
5-th percentile-10.16
Q1-5.08
median0
Q35.08
95-th percentile10.16
Maximum30.48
Range218.44
Interquartile range (IQR)10.16

Descriptive statistics

Standard deviation7.068677996
Coefficient of variation (CV)58.16573581
Kurtosis121.4987004
Mean0.1215264949
Median Absolute Deviation (MAD)5.08
Skewness-4.626998742
Sum499.96
Variance49.96620861
MonotocityNot monotonic
2021-03-08T17:31:08.818072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0712
17.3%
2.54628
15.3%
-2.54589
14.3%
5.08476
11.6%
-5.08456
11.1%
7.62331
8.0%
-7.62272
 
6.6%
10.16173
 
4.2%
-10.16161
 
3.9%
-12.795
 
2.3%
Other values (16)221
 
5.4%
ValueCountFrequency (%)
-187.961
 
< 0.1%
-33.021
 
< 0.1%
-27.941
 
< 0.1%
-25.41
 
< 0.1%
-22.863
0.1%
ValueCountFrequency (%)
30.481
 
< 0.1%
27.941
 
< 0.1%
22.862
 
< 0.1%
20.324
 
0.1%
17.7817
0.4%

reach_dif
Real number (ℝ)

ZEROS

Distinct115
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1780943121
Minimum-187.96
Maximum30.48
Zeros497
Zeros (%)12.1%
Memory size32.3 KiB
2021-03-08T17:31:09.269542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-187.96
5-th percentile-15.24
Q1-5.08
median0
Q35.08
95-th percentile12.7
Maximum30.48
Range218.44
Interquartile range (IQR)10.16

Descriptive statistics

Standard deviation9.369826656
Coefficient of variation (CV)-52.61159969
Kurtosis77.6098585
Mean-0.1780943121
Median Absolute Deviation (MAD)5.08
Skewness-3.986363894
Sum-732.68
Variance87.79365156
MonotocityNot monotonic
2021-03-08T17:31:09.670377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.54506
12.3%
-2.54499
12.1%
0497
12.1%
5.08391
9.5%
-5.08360
8.8%
7.62323
7.9%
-7.62312
7.6%
10.16223
 
5.4%
-10.16190
 
4.6%
12.7149
 
3.6%
Other values (105)664
16.1%
ValueCountFrequency (%)
-187.962
 
< 0.1%
-33.021
 
< 0.1%
-30.482
 
< 0.1%
-27.942
 
< 0.1%
-25.412
0.3%
ValueCountFrequency (%)
30.481
 
< 0.1%
27.941
 
< 0.1%
25.45
0.1%
22.867
0.2%
20.581
 
< 0.1%

age_dif
Real number (ℝ)

ZEROS

Distinct34
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4781234808
Minimum-16
Maximum17
Zeros293
Zeros (%)7.1%
Memory size32.3 KiB
2021-03-08T17:31:09.968322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-16
5-th percentile-8
Q1-3
median0
Q34
95-th percentile9
Maximum17
Range33
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.118259412
Coefficient of variation (CV)10.7048903
Kurtosis-0.07577456362
Mean0.4781234808
Median Absolute Deviation (MAD)3
Skewness-0.005259551962
Sum1967
Variance26.19657941
MonotocityNot monotonic
2021-03-08T17:31:10.233995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
-1332
 
8.1%
-2306
 
7.4%
1301
 
7.3%
2300
 
7.3%
0293
 
7.1%
3281
 
6.8%
-3272
 
6.6%
4236
 
5.7%
-4224
 
5.4%
5221
 
5.4%
Other values (24)1348
32.8%
ValueCountFrequency (%)
-161
 
< 0.1%
-152
 
< 0.1%
-147
 
0.2%
-1312
0.3%
-1226
0.6%
ValueCountFrequency (%)
171
 
< 0.1%
162
 
< 0.1%
155
 
0.1%
1418
0.4%
1316
0.4%

better_rank
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing7
Missing (%)0.2%
Memory size32.3 KiB
neither
3089 
Red
959 
Blue
 
59

Length

Max length7
Median length7
Mean length6.022887753
Min length3

Characters and Unicode

Total characters24736
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRed
2nd rowRed
3rd rowRed
4th rowRed
5th rowneither
ValueCountFrequency (%)
neither3089
75.1%
Red959
 
23.3%
Blue59
 
1.4%
(Missing)7
 
0.2%
2021-03-08T17:31:10.941470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-08T17:31:11.207114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
neither3089
75.2%
red959
 
23.4%
blue59
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e7196
29.1%
n3089
12.5%
i3089
12.5%
t3089
12.5%
h3089
12.5%
r3089
12.5%
R959
 
3.9%
d959
 
3.9%
B59
 
0.2%
l59
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23718
95.9%
Uppercase Letter1018
 
4.1%

Most frequent character per category

ValueCountFrequency (%)
e7196
30.3%
n3089
13.0%
i3089
13.0%
t3089
13.0%
h3089
13.0%
r3089
13.0%
d959
 
4.0%
l59
 
0.2%
u59
 
0.2%
ValueCountFrequency (%)
R959
94.2%
B59
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Latin24736
100.0%

Most frequent character per script

ValueCountFrequency (%)
e7196
29.1%
n3089
12.5%
i3089
12.5%
t3089
12.5%
h3089
12.5%
r3089
12.5%
R959
 
3.9%
d959
 
3.9%
B59
 
0.2%
l59
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII24736
100.0%

Most frequent character per block

ValueCountFrequency (%)
e7196
29.1%
n3089
12.5%
i3089
12.5%
t3089
12.5%
h3089
12.5%
r3089
12.5%
R959
 
3.9%
d959
 
3.9%
B59
 
0.2%
l59
 
0.2%

ganador
Categorical

HIGH CARDINALITY

Distinct1086
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Memory size32.3 KiB
Donald Cerrone
 
23
Dustin Poirier
 
19
Max Holloway
 
18
Charles Oliveira
 
18
Neil Magny
 
17
Other values (1081)
4019 

Length

Max length27
Median length13
Mean length13.10379193
Min length7

Characters and Unicode

Total characters53909
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique349 ?
Unique (%)8.5%

Sample

1st rowAlexander Volkov
2nd rowCory Sandhagen
3rd rowAlexandre Pantoja
4th rowBeneil Dariush
5th rowClay Guida
ValueCountFrequency (%)
Donald Cerrone23
 
0.6%
Dustin Poirier19
 
0.5%
Max Holloway18
 
0.4%
Charles Oliveira18
 
0.4%
Neil Magny17
 
0.4%
Jon Jones17
 
0.4%
Rafael Dos Anjos17
 
0.4%
Demian Maia16
 
0.4%
Jim Miller16
 
0.4%
Francisco Trinaldo16
 
0.4%
Other values (1076)3937
95.7%
2021-03-08T17:31:12.023068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chris57
 
0.7%
john56
 
0.7%
johnson51
 
0.6%
anthony50
 
0.6%
alex47
 
0.6%
michael46
 
0.5%
mike46
 
0.5%
silva41
 
0.5%
santos41
 
0.5%
matt40
 
0.5%
Other values (1561)7922
94.3%

Most occurring characters

ValueCountFrequency (%)
a5102
 
9.5%
e4523
 
8.4%
4284
 
7.9%
n3728
 
6.9%
i3659
 
6.8%
o3605
 
6.7%
r3510
 
6.5%
l2313
 
4.3%
s2206
 
4.1%
t1609
 
3.0%
Other values (45)19370
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter41105
76.2%
Uppercase Letter8487
 
15.7%
Space Separator4284
 
7.9%
Dash Punctuation21
 
< 0.1%
Other Punctuation12
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a5102
12.4%
e4523
11.0%
n3728
 
9.1%
i3659
 
8.9%
o3605
 
8.8%
r3510
 
8.5%
l2313
 
5.6%
s2206
 
5.4%
t1609
 
3.9%
u1305
 
3.2%
Other values (16)9545
23.2%
ValueCountFrequency (%)
M880
 
10.4%
J696
 
8.2%
S664
 
7.8%
C627
 
7.4%
D607
 
7.2%
A594
 
7.0%
R527
 
6.2%
B524
 
6.2%
T397
 
4.7%
P370
 
4.4%
Other values (15)2601
30.6%
ValueCountFrequency (%)
'8
66.7%
.4
33.3%
ValueCountFrequency (%)
4284
100.0%
ValueCountFrequency (%)
-21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49592
92.0%
Common4317
 
8.0%

Most frequent character per script

ValueCountFrequency (%)
a5102
 
10.3%
e4523
 
9.1%
n3728
 
7.5%
i3659
 
7.4%
o3605
 
7.3%
r3510
 
7.1%
l2313
 
4.7%
s2206
 
4.4%
t1609
 
3.2%
u1305
 
2.6%
Other values (41)18032
36.4%
ValueCountFrequency (%)
4284
99.2%
-21
 
0.5%
'8
 
0.2%
.4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII53909
100.0%

Most frequent character per block

ValueCountFrequency (%)
a5102
 
9.5%
e4523
 
8.4%
4284
 
7.9%
n3728
 
6.9%
i3659
 
6.8%
o3605
 
6.7%
r3510
 
6.5%
l2313
 
4.3%
s2206
 
4.1%
t1609
 
3.0%
Other values (45)19370
35.9%

Interactions

2021-03-08T17:27:12.748280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:13.217060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:13.601673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:13.929814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:14.257962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:14.617362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:14.929883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:15.273658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:15.789410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:16.133185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:16.523834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:16.883134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:17.211286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:17.601932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:17.930079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:18.283258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:18.835040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:19.475600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:19.859917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:20.278455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:20.761864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:21.304673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:22.018522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:22.628856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:23.032810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:23.395164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:23.787002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:24.172155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:24.525901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:25.052679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:25.444119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:25.949680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:26.362126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:26.827703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:27.267814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:27.832954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:28.377407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:28.781804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:29.124997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:29.451973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:30.048467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:30.459392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:30.823335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:31.176531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:31.596702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:32.055967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:32.459630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:32.856227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:27:33.271310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-08T17:30:18.973960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:19.281785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:19.613777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:19.940737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:20.268655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:20.618623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:20.940092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:21.263799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:21.583806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:21.894929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:22.230417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:22.559463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:22.910122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:23.299129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:23.649117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:24.018770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:24.368372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:24.696012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:25.032942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:25.421629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:25.901505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:26.269016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:26.996838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:27.497978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:27.838287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:28.389889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:28.809195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:29.538639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:29.879411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:30.348960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:30.632029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:30.947574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:31.253655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:31.571997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:31.850754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:32.121733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:32.570852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:32.883513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:33.244974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:33.571545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:34.023634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:34.452749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:34.796527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:35.124814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:35.468446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:35.796590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:36.140362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:36.437379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:36.927699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:37.337131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:37.728873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:38.076725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:38.421867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:38.916123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:39.361187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:39.847427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-08T17:30:40.232979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-08T17:31:12.382322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-08T17:31:13.277190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-08T17:31:14.139617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-08T17:31:15.016803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-08T17:31:15.973213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-08T17:30:40.952008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-08T17:30:45.695869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-08T17:30:46.414700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0#RedBluedatelocationcountryWinnerweight_classgenderno_of_roundsB_lossesB_total_rounds_foughtB_winsB_StanceB_Height_cmsB_Reach_cmsB_Weight_lbsR_lossesR_total_rounds_foughtR_winsR_StanceR_Height_cmsR_Reach_cmsR_Weight_lbsR_ageB_agelose_streak_difwin_streak_difwin_difloss_difheight_difreach_difage_difbetter_rankganador
000Alistair OvereemAlexander Volkov2/6/2021Las Vegas, Nevada, USAUSABlueHeavyweightMALE52266Orthodox200.66203.20250158333Orthodox193.04203.2026540320-1-27-137.620.00-8RedAlexander Volkov
111Cory SandhagenFrankie Edgar2/6/2021Las Vegas, Nevada, USAUSARedBantamweightMALE389418Orthodox167.64172.721351146Switch180.34177.80135283900127-12.70-5.0811RedCory Sandhagen
222Alexandre PantojaManel Kape2/6/2021Las Vegas, Nevada, USAUSARedFlyweightMALE3000Southpaw165.10172.721253216Orthodox165.10170.181253027-10-6-30.002.54-3RedAlexandre Pantoja
333Diego FerreiraBeneil Dariush2/6/2021Las Vegas, Nevada, USAUSABlueLightweightMALE343613Southpaw177.80182.881552218Orthodox175.26187.9615536310-1522.54-5.08-5RedBeneil Dariush
444Michael JohnsonClay Guida2/6/2021Las Vegas, Nevada, USAUSABlueLightweightMALE3158717Orthodox170.18177.80155125811Southpaw177.80185.421553439-1063-7.62-7.625neitherClay Guida
555Mike RodriguezDanilo Marques2/6/2021Las Vegas, Nevada, USAUSABlueLight HeavyweightMALE3031Orthodox198.12195.582053133Southpaw193.04208.282053235-11-2-35.08-12.703neitherDanilo Marques
677SeungWoo ChoiYoussef Zalal2/6/2021Las Vegas, Nevada, USAUSARedFeatherweightMALE31123Switch177.80182.88145291Orthodox182.88187.9614528241-12-1-5.08-5.08-4neitherSeungWoo Choi
788Dustin PoirierConor McGregor1/23/2021Abu Dhabi, Abu Dhabi, United Arab EmiratesUnited Arab EmiratesRedLightweightMALE522510Southpaw175.26187.9615566219Southpaw175.26182.88155323200-9-40.005.080NaNDustin Poirier
899Dan HookerMichael Chandler1/23/2021Abu Dhabi, Abu Dhabi, United Arab EmiratesUnited Arab EmiratesBlueLightweightMALE3032Orthodox172.72180.3415553710Switch182.88190.501553034-12-8-5-10.16-10.164NaNMichael Chandler
91111Andrew SanchezMakhmud Muradov1/23/2021Abu Dhabi, Abu Dhabi, United Arab EmiratesUnited Arab EmiratesBlueMiddleweightMALE3062Orthodox187.96190.501853225Orthodox185.42187.96185323001-3-32.542.54-2NaNMakhmud Muradov

Last rows

Unnamed: 0#RedBluedatelocationcountryWinnerweight_classgenderno_of_roundsB_lossesB_total_rounds_foughtB_winsB_StanceB_Height_cmsB_Reach_cmsB_Weight_lbsR_lossesR_total_rounds_foughtR_winsR_StanceR_Height_cmsR_Reach_cmsR_Weight_lbsR_ageB_agelose_streak_difwin_streak_difwin_difloss_difheight_difreach_difage_difbetter_rankganador
410445564556Junior Dos SantosGabriel Gonzaga3/21/2010Broomfield, Colorado, USAUSARedHeavyweightMALE33167Orthodox187.96193.04242064Orthodox193.04195.5823826300-33-3-5.08-2.54-4neitherJunior Dos Santos
410545574557Cheick KongoPaul Buentello3/21/2010Broomfield, Colorado, USAUSARedHeavyweightMALE3283Orthodox187.96195.582454227Orthodox193.04208.28240343610-42-5.08-12.70-2neitherCheick Kongo
410645584558Alessio SakaraJames Irvin3/21/2010Broomfield, Colorado, USAUSARedMiddleweightMALE34104Orthodox187.96190.502055155Orthodox182.88182.881852831-1-2-115.087.62-3neitherAlessio Sakara
410745594559Clay GuidaShannon Gugerty3/21/2010Broomfield, Colorado, USAUSARedLightweightMALE3272Orthodox177.80180.341555265Orthodox170.18177.80155282810-337.622.540neitherClay Guida
410845604560Eliot MarshallVladimir Matyushenko3/21/2010Broomfield, Colorado, USAUSABlueLight HeavyweightMALE32164Orthodox182.88187.96205073Orthodox187.96195.5820529390-21-2-5.08-7.62-10neitherVladimir Matyushenko
410945614561Duane LudwigDarren Elkins3/21/2010Broomfield, Colorado, USAUSABlueLightweightMALE3000Orthodox177.80180.34145152Orthodox177.80177.80170312510-210.002.546neitherDarren Elkins
411045624562John HowardDaniel Roberts3/21/2010Broomfield, Colorado, USAUSARedWelterweightMALE3000Southpaw177.80187.96170093Orthodox170.18180.3417027290-3-307.627.62-2neitherJohn Howard
411145634563Brendan SchaubChase Gormley3/21/2010Broomfield, Colorado, USAUSARedHeavyweightMALE3110Orthodox190.50196.00265110Orthodox193.04198.1224527270000-2.54-2.120neitherBrendan Schaub
411245644564Mike PierceJulio Paulino3/21/2010Broomfield, Colorado, USAUSARedWelterweightMALE3000Orthodox182.88185.42170161Orthodox172.72177.80170293410-1110.167.62-5neitherMike Pierce
411345654565Eric SchaferJason Brilz3/21/2010Broomfield, Colorado, USAUSABlueLight HeavyweightMALE3182Orthodox180.34180.34205393Orthodox190.50190.50185323400-12-10.16-10.16-2neitherJason Brilz